Text Generation
Transformers
Safetensors
llada2_moe
dllm
diffusion
llm
text_generation
conversational
custom_code
Instructions to use inclusionAI/LLaDA2.1-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LLaDA2.1-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.1-flash", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inclusionAI/LLaDA2.1-flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inclusionAI/LLaDA2.1-flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/LLaDA2.1-flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inclusionAI/LLaDA2.1-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/LLaDA2.1-flash
- SGLang
How to use inclusionAI/LLaDA2.1-flash with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "inclusionAI/LLaDA2.1-flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inclusionAI/LLaDA2.1-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "inclusionAI/LLaDA2.1-flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inclusionAI/LLaDA2.1-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/LLaDA2.1-flash with Docker Model Runner:
docker model run hf.co/inclusionAI/LLaDA2.1-flash
Commit History
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Use create_bidirectional_mask for backend-agnostic attention mask handling
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fix: align _init_weights with Qwen2Moe using nn.init API
Verified signatureUse @torch.no_grad() decorator, call super()._init_weights(), and only init MoE gate weights (nn.Linear/nn.Embedding handled by PreTrainedModel base class in transformers v5).c555c2f -
fix: call super()._init_weights() to match Qwen2Moe convention for transformers v5
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fix: align RotaryEmbedding with Qwen2Moe pattern for transformers compat
Verified signatureReplace custom rope_type handling with the standard Qwen2Moe pattern: use compute_default_rope_parameters for "default" type, only look up ROPE_INIT_FUNCTIONS for non-default types. Also adds partial_rotary_factor support.dfa9ac6
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Update README.md
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Update README.md
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Update modeling_llada2_moe.py
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Update README.md
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Create modeling_llada2_moe.py
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Update configuration_llada2_moe.py
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Create README.md
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Add files using upload-large-folder tool
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initial commit
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